Abstract
Transcription Factors (TFs) bind to DNA and control activity of target genes. Here, we present ChIPanalyser, a user-friendly, versatile and powerful R/Bioconductor package predicting and modelling the binding of TFs to DNA. ChIPanalyser performs similarly to state-of-the-art tools, but is an explainable model and provides biological insights into binding mechanisms of TFs. We focused on investigating the binding mechanisms of three TFs that are known architectural proteins CTCF, BEAF-32 and su(Hw) in three Drosophila cell lines (BG3, Kc167 and S2). While CTCF preferentially binds only to a subset of high affinity sites located mainly in open chromatin, BEAF-32 binds to most of its high affinity binding sites available in open chromatin. In contrast, su(Hw) binds to both open chromatin and also partially closed chromatin. Most importantly, differences in TF binding profiles between cell lines for these TFs are mainly driven by differences in DNA accessibility and not by differences in TF concentrations between cell lines. Finally, we investigated binding of Hox TFs in Drosophila and found that Ubx binds only in open chromatin, while Abd-B and Dfd are capable to bind in both open and partially closed chromatin. Overall, our results show that TFs display different binding mechanisms and that our model is able to recapitulate their specific binding behaviour.
Highlights
Decades of research have shown that gene expression plays an essential role in the livelihood of cells and organisms
In some cases, selecting the optimal parameters was hindered by little variation in correlation between parameter combinations and, the selection of these parameters was exclusively driven by Mean Squared Error (MSE)
The Sigmoid method showed a slight signal reduction in smaller peaks, which was translated into a slight improvement of the mean Area Under Curve Receiver Operator Characteristic (AUC ROC) score between ChIP signal and our predictions
Summary
Decades of research have shown that gene expression plays an essential role in the livelihood of cells and organisms. The most commonly used experimental method to determine specific regions of DNA where TFs bind is chromatin immunoprecipitation followed by sequencing (ChIP-seq) [1, 2]. This technique has become the gold standard to determine the binding profiles of TFs to the genome, but, despite the huge impact on understanding gene regulation, it does not provide a mechanistic model of what drives the binding of TFs to those regions or even how genes are regulated. While we still lack a complete predictive model for gene expression, over the years, many factors have been identified as contributing to context dependant TF binding
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